{"cells":[{"metadata":{},"cell_type":"markdown","source":"## 1 模型训练"},{"metadata":{"trusted":true},"cell_type":"code","source":"from MMEdu import MMClassification as cls\nmodel = cls(backbone='MobileNet')\nmodel.num_classes = 2\nmodel.load_dataset(path='/data/PTVSMN/tudou_dataset') \nmodel.save_fold = 'checkpoints/cls_model/tudou_mobilenet_6'\n\nmodel.train(epochs=10,checkpoint='checkpoints/pretrain_model/mobilenet_v2.pth',batch_size=4,lr=0.005,validate=True,device='cuda')","execution_count":8,"outputs":[{"output_type":"stream","text":"2023-01-28 14:49:34,377 - mmcls - INFO - Start running, host: openinnolab@xlabb42f939725c9682c49b4f02c290995c0-0, work_dir: /home/openinnolab/work/current/checkpoints/cls_model/tudou_mobilenet_6\n2023-01-28 14:49:34,378 - mmcls - INFO - Hooks will be executed in the following order:\nbefore_run:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(NORMAL      ) CheckpointHook                     \n(LOW         ) EvalHook                           \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_epoch:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(LOW         ) IterTimerHook                      \n(LOW         ) EvalHook                           \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_iter:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(LOW         ) IterTimerHook                      \n(LOW         ) EvalHook                           \n -------------------- \nafter_train_iter:\n(ABOVE_NORMAL) OptimizerHook                      \n(NORMAL      ) CheckpointHook                     \n(LOW         ) IterTimerHook                      \n(LOW         ) EvalHook                           \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_train_epoch:\n(NORMAL      ) CheckpointHook                     \n(LOW         ) EvalHook                           \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_epoch:\n(LOW         ) IterTimerHook                      \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_epoch:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_run:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \n2023-01-28 14:49:34,378 - mmcls - INFO - workflow: [('train', 1)], max: 10 epochs\n2023-01-28 14:49:34,379 - mmcls - INFO - Checkpoints will be saved to /home/openinnolab/work/current/checkpoints/cls_model/tudou_mobilenet_6 by HardDiskBackend.\n","name":"stderr"},{"output_type":"stream","text":"load checkpoint from local path: checkpoints/pretrain_model/mobilenet_v2.pth\nThe model and loaded state dict do not match exactly\n\nsize mismatch for head.fc.weight: copying a param with shape torch.Size([1000, 1280]) from checkpoint, the shape in current model is torch.Size([2, 1280]).\nsize mismatch for head.fc.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([2]).\n","name":"stdout"},{"output_type":"stream","text":"2023-01-28 14:49:37,041 - mmcls - INFO - Epoch [1][10/318]\tlr: 5.000e-03, eta: 0:14:02, time: 0.266, data_time: 0.214, memory: 332, loss: 0.9719\n2023-01-28 14:49:37,587 - mmcls - INFO - Epoch [1][20/318]\tlr: 5.000e-03, eta: 0:08:25, time: 0.055, data_time: 0.004, memory: 332, loss: 1.6872\n2023-01-28 14:49:38,093 - mmcls - INFO - Epoch [1][30/318]\tlr: 5.000e-03, eta: 0:06:29, time: 0.051, data_time: 0.001, memory: 332, loss: 2.9748\n2023-01-28 14:49:38,609 - mmcls - INFO - Epoch [1][40/318]\tlr: 5.000e-03, eta: 0:05:31, time: 0.052, data_time: 0.001, memory: 332, loss: 4.2208\n2023-01-28 14:49:39,109 - mmcls - INFO - Epoch [1][50/318]\tlr: 5.000e-03, eta: 0:04:55, time: 0.049, data_time: 0.001, memory: 332, loss: 3.5813\n2023-01-28 14:49:39,605 - mmcls - INFO - Epoch [1][60/318]\tlr: 5.000e-03, eta: 0:04:30, time: 0.049, data_time: 0.001, memory: 332, loss: 1.4107\n2023-01-28 14:49:40,097 - mmcls - INFO - Epoch [1][70/318]\tlr: 5.000e-03, eta: 0:04:13, time: 0.050, data_time: 0.002, memory: 332, loss: 2.1625\n2023-01-28 14:49:40,595 - mmcls - INFO - Epoch [1][80/318]\tlr: 5.000e-03, eta: 0:04:00, time: 0.050, data_time: 0.001, memory: 332, loss: 1.1349\n2023-01-28 14:49:41,025 - mmcls - INFO - Epoch [1][90/318]\tlr: 5.000e-03, eta: 0:03:47, time: 0.043, data_time: 0.001, memory: 332, loss: 1.2315\n2023-01-28 14:49:41,519 - mmcls - INFO - Epoch [1][100/318]\tlr: 5.000e-03, eta: 0:03:39, time: 0.049, data_time: 0.000, memory: 332, loss: 1.4118\n2023-01-28 14:49:41,993 - mmcls - INFO - Epoch [1][110/318]\tlr: 5.000e-03, eta: 0:03:32, time: 0.048, data_time: 0.002, memory: 332, loss: 1.0857\n2023-01-28 14:49:42,487 - mmcls - INFO - Epoch [1][120/318]\tlr: 5.000e-03, eta: 0:03:26, time: 0.049, data_time: 0.000, memory: 332, loss: 2.2497\n2023-01-28 14:49:42,980 - mmcls - INFO - Epoch [1][130/318]\tlr: 5.000e-03, eta: 0:03:21, time: 0.049, data_time: 0.001, memory: 332, loss: 1.0567\n2023-01-28 14:49:43,441 - mmcls - INFO - Epoch [1][140/318]\tlr: 5.000e-03, eta: 0:03:16, time: 0.046, data_time: 0.001, memory: 332, loss: 1.0876\n2023-01-28 14:49:43,978 - mmcls - INFO - Epoch [1][150/318]\tlr: 5.000e-03, eta: 0:03:13, time: 0.054, data_time: 0.001, memory: 332, loss: 0.6089\n2023-01-28 14:49:44,506 - mmcls - INFO - Epoch [1][160/318]\tlr: 5.000e-03, eta: 0:03:10, time: 0.052, data_time: 0.001, memory: 332, loss: 0.6627\n2023-01-28 14:49:45,010 - mmcls - INFO - Epoch [1][170/318]\tlr: 5.000e-03, eta: 0:03:08, time: 0.051, data_time: 0.002, memory: 332, loss: 0.8716\n2023-01-28 14:49:45,526 - mmcls - INFO - Epoch [1][180/318]\tlr: 5.000e-03, eta: 0:03:05, time: 0.052, data_time: 0.001, memory: 332, loss: 0.5080\n2023-01-28 14:49:46,000 - mmcls - INFO - Epoch [1][190/318]\tlr: 5.000e-03, eta: 0:03:02, time: 0.047, data_time: 0.001, memory: 332, loss: 1.9687\n2023-01-28 14:49:46,432 - mmcls - INFO - Epoch [1][200/318]\tlr: 5.000e-03, eta: 0:02:59, time: 0.043, data_time: 0.000, memory: 332, loss: 2.1407\n2023-01-28 14:49:46,912 - mmcls - INFO - Epoch [1][210/318]\tlr: 5.000e-03, eta: 0:02:57, time: 0.048, data_time: 0.000, memory: 332, loss: 0.9394\n2023-01-28 14:49:47,382 - mmcls - INFO - Epoch [1][220/318]\tlr: 5.000e-03, eta: 0:02:54, time: 0.047, data_time: 0.000, memory: 332, loss: 0.8340\n2023-01-28 14:49:47,831 - mmcls - INFO - Epoch [1][230/318]\tlr: 5.000e-03, eta: 0:02:52, time: 0.045, data_time: 0.000, memory: 332, loss: 0.7167\n2023-01-28 14:49:48,387 - mmcls - INFO - Epoch [1][240/318]\tlr: 5.000e-03, eta: 0:02:51, time: 0.056, data_time: 0.001, memory: 332, loss: 0.9290\n2023-01-28 14:49:48,893 - mmcls - INFO - Epoch [1][250/318]\tlr: 5.000e-03, eta: 0:02:50, time: 0.051, data_time: 0.001, memory: 332, loss: 0.6025\n2023-01-28 14:49:49,378 - mmcls - INFO - Epoch [1][260/318]\tlr: 5.000e-03, eta: 0:02:48, time: 0.049, data_time: 0.001, memory: 332, loss: 1.3738\n2023-01-28 14:49:49,816 - mmcls - INFO - Epoch [1][270/318]\tlr: 5.000e-03, eta: 0:02:46, time: 0.044, data_time: 0.001, memory: 332, loss: 0.5382\n2023-01-28 14:49:50,301 - mmcls - INFO - Epoch [1][280/318]\tlr: 5.000e-03, eta: 0:02:44, time: 0.049, data_time: 0.001, memory: 332, loss: 0.5321\n2023-01-28 14:49:50,831 - mmcls - INFO - Epoch [1][290/318]\tlr: 5.000e-03, eta: 0:02:43, time: 0.053, data_time: 0.001, memory: 332, loss: 0.5741\n2023-01-28 14:49:51,395 - mmcls - INFO - Epoch [1][300/318]\tlr: 5.000e-03, eta: 0:02:43, time: 0.056, data_time: 0.001, memory: 332, loss: 0.9016\n2023-01-28 14:49:51,891 - mmcls - INFO - Epoch [1][310/318]\tlr: 5.000e-03, eta: 0:02:42, time: 0.050, data_time: 0.001, memory: 332, loss: 0.7480\n2023-01-28 14:49:52,215 - mmcls - INFO - Saving checkpoint at 1 epochs\n","name":"stderr"},{"output_type":"stream","text":"[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 159/159, 135.3 task/s, elapsed: 1s, ETA:     0s","name":"stdout"},{"output_type":"stream","text":"2023-01-28 14:49:53,673 - mmcls - INFO - Now best checkpoint is saved as best_accuracy_top-1_epoch_1.pth.\n2023-01-28 14:49:53,674 - mmcls - INFO - Best accuracy_top-1 is 78.6163 at 1 epoch.\n2023-01-28 14:49:53,675 - mmcls - INFO - Epoch(val) [1][40]\taccuracy_top-1: 78.6163\n2023-01-28 14:49:56,203 - mmcls - INFO - Epoch [2][10/318]\tlr: 4.900e-03, eta: 0:02:54, time: 0.252, data_time: 0.202, memory: 332, loss: 0.4917\n2023-01-28 14:49:56,640 - mmcls - INFO - Epoch [2][20/318]\tlr: 4.900e-03, eta: 0:02:52, time: 0.044, data_time: 0.001, memory: 332, loss: 0.2303\n2023-01-28 14:49:57,105 - mmcls - INFO - Epoch [2][30/318]\tlr: 4.900e-03, eta: 0:02:50, time: 0.046, data_time: 0.000, memory: 332, loss: 0.6953\n2023-01-28 14:49:57,580 - mmcls - INFO - Epoch [2][40/318]\tlr: 4.900e-03, eta: 0:02:48, time: 0.048, data_time: 0.001, memory: 332, loss: 0.8765\n2023-01-28 14:49:58,076 - mmcls - INFO - Epoch [2][50/318]\tlr: 4.900e-03, eta: 0:02:47, time: 0.046, data_time: 0.000, memory: 332, loss: 0.5635\n2023-01-28 14:49:58,577 - mmcls - INFO - Epoch [2][60/318]\tlr: 4.900e-03, eta: 0:02:46, time: 0.054, data_time: 0.004, memory: 332, loss: 0.3603\n2023-01-28 14:49:59,099 - mmcls - INFO - Epoch [2][70/318]\tlr: 4.900e-03, eta: 0:02:44, time: 0.052, data_time: 0.001, memory: 332, loss: 0.6190\n2023-01-28 14:49:59,616 - mmcls - INFO - Epoch [2][80/318]\tlr: 4.900e-03, eta: 0:02:43, time: 0.052, data_time: 0.001, memory: 332, loss: 0.5038\n2023-01-28 14:50:00,126 - mmcls - INFO - Epoch [2][90/318]\tlr: 4.900e-03, eta: 0:02:42, time: 0.051, data_time: 0.001, memory: 332, loss: 0.5877\n2023-01-28 14:50:00,628 - mmcls - INFO - Epoch [2][100/318]\tlr: 4.900e-03, eta: 0:02:41, time: 0.049, data_time: 0.001, memory: 332, loss: 0.5200\n2023-01-28 14:50:01,136 - mmcls - INFO - Epoch [2][110/318]\tlr: 4.900e-03, eta: 0:02:40, time: 0.051, data_time: 0.001, memory: 332, loss: 0.6115\n2023-01-28 14:50:01,896 - mmcls - INFO - Epoch [2][120/318]\tlr: 4.900e-03, eta: 0:02:40, time: 0.076, data_time: 0.001, memory: 332, loss: 0.4693\n2023-01-28 14:50:02,589 - mmcls - INFO - Epoch [2][130/318]\tlr: 4.900e-03, eta: 0:02:41, time: 0.070, data_time: 0.002, memory: 332, loss: 0.5595\n2023-01-28 14:50:03,077 - mmcls - INFO - Epoch [2][140/318]\tlr: 4.900e-03, eta: 0:02:39, time: 0.049, data_time: 0.001, memory: 332, loss: 0.3841\n2023-01-28 14:50:03,590 - mmcls - INFO - Epoch [2][150/318]\tlr: 4.900e-03, eta: 0:02:38, time: 0.051, data_time: 0.001, memory: 332, loss: 0.8003\n2023-01-28 14:50:04,077 - mmcls - INFO - Epoch [2][160/318]\tlr: 4.900e-03, eta: 0:02:37, time: 0.049, data_time: 0.002, memory: 332, loss: 0.4978\n2023-01-28 14:50:04,577 - mmcls - INFO - Epoch [2][170/318]\tlr: 4.900e-03, eta: 0:02:36, time: 0.050, data_time: 0.001, memory: 332, loss: 0.6811\n2023-01-28 14:50:05,035 - mmcls - INFO - Epoch [2][180/318]\tlr: 4.900e-03, eta: 0:02:35, time: 0.046, data_time: 0.001, memory: 332, loss: 1.5770\n2023-01-28 14:50:05,576 - mmcls - INFO - Epoch [2][190/318]\tlr: 4.900e-03, eta: 0:02:34, time: 0.054, data_time: 0.005, memory: 332, loss: 0.4887\n2023-01-28 14:50:06,160 - mmcls - INFO - Epoch [2][200/318]\tlr: 4.900e-03, eta: 0:02:34, time: 0.058, data_time: 0.001, memory: 332, loss: 0.5995\n2023-01-28 14:50:06,975 - mmcls - INFO - Epoch [2][210/318]\tlr: 4.900e-03, eta: 0:02:34, time: 0.081, data_time: 0.001, memory: 332, loss: 0.4791\n2023-01-28 14:50:07,677 - mmcls - INFO - Epoch [2][220/318]\tlr: 4.900e-03, eta: 0:02:34, time: 0.070, data_time: 0.001, memory: 332, loss: 0.3304\n2023-01-28 14:50:08,337 - mmcls - INFO - Epoch [2][230/318]\tlr: 4.900e-03, eta: 0:02:34, time: 0.066, data_time: 0.001, memory: 332, loss: 0.5060\n2023-01-28 14:50:09,288 - mmcls - INFO - Epoch [2][240/318]\tlr: 4.900e-03, eta: 0:02:35, time: 0.095, data_time: 0.001, memory: 332, loss: 0.4616\n2023-01-28 14:50:10,178 - mmcls - INFO - Epoch [2][250/318]\tlr: 4.900e-03, eta: 0:02:36, time: 0.089, data_time: 0.001, memory: 332, loss: 0.4932\n2023-01-28 14:50:10,834 - mmcls - INFO - Epoch [2][260/318]\tlr: 4.900e-03, eta: 0:02:36, time: 0.066, data_time: 0.001, memory: 332, loss: 0.4758\n2023-01-28 14:50:11,404 - mmcls - INFO - Epoch [2][270/318]\tlr: 4.900e-03, eta: 0:02:35, time: 0.057, data_time: 0.001, memory: 332, loss: 0.4433\n2023-01-28 14:50:11,829 - mmcls - INFO - Epoch [2][280/318]\tlr: 4.900e-03, eta: 0:02:33, time: 0.043, data_time: 0.001, memory: 332, loss: 0.3168\n2023-01-28 14:50:12,332 - mmcls - INFO - Epoch [2][290/318]\tlr: 4.900e-03, eta: 0:02:32, time: 0.050, data_time: 0.000, memory: 332, loss: 0.5510\n2023-01-28 14:50:12,818 - mmcls - INFO - Epoch [2][300/318]\tlr: 4.900e-03, eta: 0:02:31, time: 0.049, data_time: 0.001, memory: 332, loss: 0.4120\n2023-01-28 14:50:13,321 - mmcls - INFO - Epoch [2][310/318]\tlr: 4.900e-03, eta: 0:02:30, time: 0.049, data_time: 0.001, memory: 332, loss: 0.2714\n2023-01-28 14:50:13,645 - mmcls - INFO - Saving checkpoint at 2 epochs\n","name":"stderr"},{"output_type":"stream","text":"[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 159/159, 143.4 task/s, elapsed: 1s, ETA:     0s","name":"stdout"},{"output_type":"stream","text":"2023-01-28 14:50:14,883 - mmcls - INFO - The previous best checkpoint /home/openinnolab/work/current/checkpoints/cls_model/tudou_mobilenet_6/best_accuracy_top-1_epoch_1.pth was removed\n2023-01-28 14:50:14,982 - mmcls - INFO - Now best checkpoint is saved as best_accuracy_top-1_epoch_2.pth.\n2023-01-28 14:50:14,982 - mmcls - INFO - Best accuracy_top-1 is 89.3082 at 2 epoch.\n2023-01-28 14:50:14,983 - mmcls - INFO - Epoch(val) [2][40]\taccuracy_top-1: 89.3082\n2023-01-28 14:50:17,500 - mmcls - INFO - Epoch [3][10/318]\tlr: 4.802e-03, eta: 0:02:35, time: 0.251, data_time: 0.203, memory: 332, loss: 0.1154\n2023-01-28 14:50:17,932 - mmcls - INFO - Epoch [3][20/318]\tlr: 4.802e-03, eta: 0:02:34, time: 0.043, data_time: 0.001, memory: 332, loss: 0.4472\n2023-01-28 14:50:18,404 - mmcls - INFO - Epoch [3][30/318]\tlr: 4.802e-03, eta: 0:02:33, time: 0.047, data_time: 0.000, memory: 332, loss: 0.5526\n2023-01-28 14:50:18,889 - mmcls - INFO - Epoch [3][40/318]\tlr: 4.802e-03, eta: 0:02:32, time: 0.048, data_time: 0.001, memory: 332, loss: 0.3628\n2023-01-28 14:50:19,325 - mmcls - INFO - Epoch [3][50/318]\tlr: 4.802e-03, eta: 0:02:30, time: 0.043, data_time: 0.000, memory: 332, loss: 0.2962\n2023-01-28 14:50:19,818 - mmcls - INFO - Epoch [3][60/318]\tlr: 4.802e-03, eta: 0:02:29, time: 0.049, data_time: 0.001, memory: 332, loss: 0.3301\n2023-01-28 14:50:20,292 - mmcls - INFO - Epoch [3][70/318]\tlr: 4.802e-03, eta: 0:02:28, time: 0.047, data_time: 0.001, memory: 332, loss: 0.1717\n2023-01-28 14:50:20,731 - mmcls - INFO - Epoch [3][80/318]\tlr: 4.802e-03, eta: 0:02:27, time: 0.044, data_time: 0.001, memory: 332, loss: 0.2504\n2023-01-28 14:50:21,201 - mmcls - INFO - Epoch [3][90/318]\tlr: 4.802e-03, eta: 0:02:26, time: 0.047, data_time: 0.001, memory: 332, loss: 0.3010\n2023-01-28 14:50:21,687 - mmcls - INFO - Epoch [3][100/318]\tlr: 4.802e-03, eta: 0:02:25, time: 0.049, data_time: 0.001, memory: 332, loss: 0.3374\n2023-01-28 14:50:22,130 - mmcls - INFO - Epoch [3][110/318]\tlr: 4.802e-03, eta: 0:02:24, time: 0.044, data_time: 0.001, memory: 332, loss: 0.3444\n2023-01-28 14:50:22,612 - mmcls - INFO - Epoch [3][120/318]\tlr: 4.802e-03, eta: 0:02:23, time: 0.048, data_time: 0.001, memory: 332, loss: 0.2503\n2023-01-28 14:50:23,079 - mmcls - INFO - Epoch [3][130/318]\tlr: 4.802e-03, eta: 0:02:22, time: 0.047, data_time: 0.001, memory: 332, loss: 0.4124\n2023-01-28 14:50:23,520 - mmcls - INFO - Epoch [3][140/318]\tlr: 4.802e-03, eta: 0:02:21, time: 0.044, data_time: 0.000, memory: 332, loss: 0.4343\n2023-01-28 14:50:23,992 - mmcls - INFO - Epoch [3][150/318]\tlr: 4.802e-03, eta: 0:02:20, time: 0.047, data_time: 0.000, memory: 332, loss: 0.3753\n2023-01-28 14:50:24,434 - mmcls - INFO - Epoch [3][160/318]\tlr: 4.802e-03, eta: 0:02:19, time: 0.044, data_time: 0.000, memory: 332, loss: 0.5364\n2023-01-28 14:50:24,903 - mmcls - INFO - Epoch [3][170/318]\tlr: 4.802e-03, eta: 0:02:18, time: 0.047, data_time: 0.001, memory: 332, loss: 0.3584\n2023-01-28 14:50:25,384 - mmcls - INFO - Epoch [3][180/318]\tlr: 4.802e-03, eta: 0:02:17, time: 0.048, data_time: 0.000, memory: 332, loss: 0.4144\n2023-01-28 14:50:25,815 - mmcls - INFO - Epoch [3][190/318]\tlr: 4.802e-03, eta: 0:02:16, time: 0.043, data_time: 0.000, memory: 332, loss: 0.3725\n2023-01-28 14:50:26,299 - mmcls - INFO - Epoch [3][200/318]\tlr: 4.802e-03, eta: 0:02:15, time: 0.048, data_time: 0.000, memory: 332, loss: 0.3773\n2023-01-28 14:50:26,738 - mmcls - INFO - Epoch [3][210/318]\tlr: 4.802e-03, eta: 0:02:14, time: 0.044, data_time: 0.001, memory: 332, loss: 0.2353\n2023-01-28 14:50:27,202 - mmcls - INFO - Epoch [3][220/318]\tlr: 4.802e-03, eta: 0:02:13, time: 0.046, data_time: 0.001, memory: 332, loss: 0.4188\n2023-01-28 14:50:27,633 - mmcls - INFO - Epoch [3][230/318]\tlr: 4.802e-03, eta: 0:02:13, time: 0.043, data_time: 0.001, memory: 332, loss: 0.3489\n2023-01-28 14:50:28,107 - mmcls - INFO - Epoch [3][240/318]\tlr: 4.802e-03, eta: 0:02:12, time: 0.047, data_time: 0.000, memory: 332, loss: 0.4173\n2023-01-28 14:50:28,598 - mmcls - INFO - Epoch [3][250/318]\tlr: 4.802e-03, eta: 0:02:11, time: 0.048, data_time: 0.001, memory: 332, loss: 0.2523\n2023-01-28 14:50:29,036 - mmcls - INFO - Epoch [3][260/318]\tlr: 4.802e-03, eta: 0:02:10, time: 0.044, data_time: 0.001, memory: 332, loss: 0.3500\n2023-01-28 14:50:29,640 - mmcls - INFO - Epoch [3][270/318]\tlr: 4.802e-03, eta: 0:02:09, time: 0.059, data_time: 0.000, memory: 332, loss: 0.1401\n2023-01-28 14:50:30,345 - mmcls - INFO - Epoch [3][280/318]\tlr: 4.802e-03, eta: 0:02:09, time: 0.071, data_time: 0.001, memory: 332, loss: 0.4335\n2023-01-28 14:50:30,865 - mmcls - INFO - Epoch [3][290/318]\tlr: 4.802e-03, eta: 0:02:09, time: 0.052, data_time: 0.000, memory: 332, loss: 0.2072\n2023-01-28 14:50:31,331 - mmcls - INFO - Epoch [3][300/318]\tlr: 4.802e-03, eta: 0:02:08, time: 0.046, data_time: 0.000, memory: 332, loss: 0.4770\n2023-01-28 14:50:31,794 - mmcls - INFO - Epoch [3][310/318]\tlr: 4.802e-03, eta: 0:02:07, time: 0.047, data_time: 0.001, memory: 332, loss: 0.2803\n2023-01-28 14:50:32,102 - mmcls - INFO - Saving checkpoint at 3 epochs\n","name":"stderr"},{"output_type":"stream","text":"[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 159/159, 146.0 task/s, elapsed: 1s, ETA:     0s","name":"stdout"},{"output_type":"stream","text":"2023-01-28 14:50:33,317 - mmcls - INFO - The previous best checkpoint /home/openinnolab/work/current/checkpoints/cls_model/tudou_mobilenet_6/best_accuracy_top-1_epoch_2.pth was removed\n2023-01-28 14:50:33,431 - mmcls - INFO - Now best checkpoint is saved as best_accuracy_top-1_epoch_3.pth.\n2023-01-28 14:50:33,431 - mmcls - INFO - Best accuracy_top-1 is 92.4528 at 3 epoch.\n2023-01-28 14:50:33,432 - mmcls - INFO - Epoch(val) [3][40]\taccuracy_top-1: 92.4528\n2023-01-28 14:50:35,894 - mmcls - INFO - Epoch [4][10/318]\tlr: 4.706e-03, eta: 0:02:09, time: 0.245, data_time: 0.202, memory: 332, loss: 0.3913\n2023-01-28 14:50:36,377 - mmcls - INFO - Epoch [4][20/318]\tlr: 4.706e-03, eta: 0:02:08, time: 0.049, data_time: 0.001, memory: 332, loss: 0.3459\n2023-01-28 14:50:36,826 - mmcls - INFO - Epoch [4][30/318]\tlr: 4.706e-03, eta: 0:02:07, time: 0.045, data_time: 0.001, memory: 332, loss: 0.5117\n2023-01-28 14:50:37,315 - mmcls - INFO - Epoch [4][40/318]\tlr: 4.706e-03, eta: 0:02:07, time: 0.049, data_time: 0.000, memory: 332, loss: 0.2563\n2023-01-28 14:50:37,786 - mmcls - INFO - Epoch [4][50/318]\tlr: 4.706e-03, eta: 0:02:06, time: 0.047, data_time: 0.001, memory: 332, loss: 0.3375\n2023-01-28 14:50:38,284 - mmcls - INFO - Epoch [4][60/318]\tlr: 4.706e-03, eta: 0:02:05, time: 0.050, data_time: 0.000, memory: 332, loss: 0.3576\n2023-01-28 14:50:38,730 - mmcls - INFO - Epoch [4][70/318]\tlr: 4.706e-03, eta: 0:02:04, time: 0.044, data_time: 0.001, memory: 332, loss: 0.1505\n2023-01-28 14:50:39,203 - mmcls - INFO - Epoch [4][80/318]\tlr: 4.706e-03, eta: 0:02:03, time: 0.048, data_time: 0.001, memory: 332, loss: 0.1450\n2023-01-28 14:50:39,707 - mmcls - INFO - Epoch [4][90/318]\tlr: 4.706e-03, eta: 0:02:03, time: 0.049, data_time: 0.000, memory: 332, loss: 0.5105\n2023-01-28 14:50:40,192 - mmcls - INFO - Epoch [4][100/318]\tlr: 4.706e-03, eta: 0:02:02, time: 0.050, data_time: 0.002, memory: 332, loss: 0.5429\n2023-01-28 14:50:40,679 - mmcls - INFO - Epoch [4][110/318]\tlr: 4.706e-03, eta: 0:02:01, time: 0.049, data_time: 0.001, memory: 332, loss: 0.2763\n2023-01-28 14:50:41,146 - mmcls - INFO - Epoch [4][120/318]\tlr: 4.706e-03, eta: 0:02:00, time: 0.046, data_time: 0.000, memory: 332, loss: 0.4447\n2023-01-28 14:50:41,625 - mmcls - INFO - Epoch [4][130/318]\tlr: 4.706e-03, eta: 0:02:00, time: 0.049, data_time: 0.002, memory: 332, loss: 0.5764\n2023-01-28 14:50:42,114 - mmcls - INFO - Epoch [4][140/318]\tlr: 4.706e-03, eta: 0:01:59, time: 0.049, data_time: 0.000, memory: 332, loss: 0.1147\n2023-01-28 14:50:42,642 - mmcls - INFO - Epoch [4][150/318]\tlr: 4.706e-03, eta: 0:01:58, time: 0.053, data_time: 0.001, memory: 332, loss: 0.1913\n2023-01-28 14:50:43,157 - mmcls - INFO - Epoch [4][160/318]\tlr: 4.706e-03, eta: 0:01:58, time: 0.052, data_time: 0.000, memory: 332, loss: 0.3860\n2023-01-28 14:50:43,717 - mmcls - INFO - Epoch [4][170/318]\tlr: 4.706e-03, eta: 0:01:57, time: 0.056, data_time: 0.000, memory: 332, loss: 0.4740\n2023-01-28 14:50:44,594 - mmcls - INFO - Epoch [4][180/318]\tlr: 4.706e-03, eta: 0:01:57, time: 0.087, data_time: 0.000, memory: 332, loss: 0.3885\n2023-01-28 14:50:45,684 - mmcls - INFO - Epoch [4][190/318]\tlr: 4.706e-03, eta: 0:01:57, time: 0.109, data_time: 0.001, memory: 332, loss: 0.3765\n2023-01-28 14:50:46,694 - mmcls - INFO - Epoch [4][200/318]\tlr: 4.706e-03, eta: 0:01:57, time: 0.101, data_time: 0.001, memory: 332, loss: 0.5873\n2023-01-28 14:50:47,497 - mmcls - INFO - Epoch [4][210/318]\tlr: 4.706e-03, eta: 0:01:57, time: 0.080, data_time: 0.001, memory: 332, loss: 0.4874\n2023-01-28 14:50:48,156 - mmcls - INFO - Epoch [4][220/318]\tlr: 4.706e-03, eta: 0:01:57, time: 0.066, data_time: 0.001, memory: 332, loss: 0.3541\n2023-01-28 14:50:48,756 - mmcls - INFO - Epoch [4][230/318]\tlr: 4.706e-03, eta: 0:01:56, time: 0.060, data_time: 0.001, memory: 332, loss: 0.5346\n2023-01-28 14:50:49,326 - mmcls - INFO - Epoch [4][240/318]\tlr: 4.706e-03, eta: 0:01:56, time: 0.057, data_time: 0.001, memory: 332, loss: 0.3548\n2023-01-28 14:50:49,887 - mmcls - INFO - Epoch [4][250/318]\tlr: 4.706e-03, eta: 0:01:55, time: 0.056, data_time: 0.001, memory: 332, loss: 0.1983\n2023-01-28 14:50:50,398 - mmcls - INFO - Epoch [4][260/318]\tlr: 4.706e-03, eta: 0:01:54, time: 0.051, data_time: 0.001, memory: 332, loss: 0.3075\n2023-01-28 14:50:50,928 - mmcls - INFO - Epoch [4][270/318]\tlr: 4.706e-03, eta: 0:01:54, time: 0.053, data_time: 0.001, memory: 332, loss: 0.2540\n2023-01-28 14:50:51,485 - mmcls - INFO - Epoch [4][280/318]\tlr: 4.706e-03, eta: 0:01:53, time: 0.056, data_time: 0.002, memory: 332, loss: 0.2868\n2023-01-28 14:50:52,017 - mmcls - INFO - Epoch [4][290/318]\tlr: 4.706e-03, eta: 0:01:52, time: 0.053, data_time: 0.000, memory: 332, loss: 0.2076\n2023-01-28 14:50:52,539 - mmcls - INFO - Epoch [4][300/318]\tlr: 4.706e-03, eta: 0:01:52, time: 0.052, data_time: 0.001, memory: 332, loss: 0.1542\n2023-01-28 14:50:53,113 - mmcls - INFO - Epoch [4][310/318]\tlr: 4.706e-03, eta: 0:01:51, time: 0.058, data_time: 0.001, memory: 332, loss: 0.2773\n2023-01-28 14:50:53,537 - mmcls - INFO - Saving checkpoint at 4 epochs\n","name":"stderr"},{"output_type":"stream","text":"[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 159/159, 162.8 task/s, elapsed: 1s, ETA:     0s","name":"stdout"},{"output_type":"stream","text":"2023-01-28 14:50:54,636 - mmcls - INFO - The previous best checkpoint /home/openinnolab/work/current/checkpoints/cls_model/tudou_mobilenet_6/best_accuracy_top-1_epoch_3.pth was removed\n2023-01-28 14:50:54,778 - mmcls - INFO - Now best checkpoint is saved as best_accuracy_top-1_epoch_4.pth.\n2023-01-28 14:50:54,778 - mmcls - INFO - Best accuracy_top-1 is 94.9686 at 4 epoch.\n2023-01-28 14:50:54,779 - mmcls - INFO - Epoch(val) [4][40]\taccuracy_top-1: 94.9686\n2023-01-28 14:50:57,407 - mmcls - INFO - Epoch [5][10/318]\tlr: 4.612e-03, eta: 0:01:52, time: 0.262, data_time: 0.202, memory: 332, loss: 0.1240\n2023-01-28 14:50:57,881 - mmcls - INFO - Epoch [5][20/318]\tlr: 4.612e-03, eta: 0:01:52, time: 0.048, data_time: 0.001, memory: 332, loss: 0.6619\n2023-01-28 14:50:58,318 - mmcls - INFO - Epoch [5][30/318]\tlr: 4.612e-03, eta: 0:01:51, time: 0.044, data_time: 0.000, memory: 332, loss: 0.3249\n2023-01-28 14:50:58,816 - mmcls - INFO - Epoch [5][40/318]\tlr: 4.612e-03, eta: 0:01:50, time: 0.049, data_time: 0.001, memory: 332, loss: 0.4149\n2023-01-28 14:50:59,287 - mmcls - INFO - Epoch [5][50/318]\tlr: 4.612e-03, eta: 0:01:49, time: 0.047, data_time: 0.001, memory: 332, loss: 0.3759\n2023-01-28 14:50:59,776 - mmcls - INFO - Epoch [5][60/318]\tlr: 4.612e-03, eta: 0:01:49, time: 0.045, data_time: 0.000, memory: 332, loss: 0.5407\n2023-01-28 14:51:00,219 - mmcls - INFO - Epoch [5][70/318]\tlr: 4.612e-03, eta: 0:01:48, time: 0.048, data_time: 0.005, memory: 332, loss: 0.2062\n2023-01-28 14:51:00,694 - mmcls - INFO - Epoch [5][80/318]\tlr: 4.612e-03, eta: 0:01:47, time: 0.048, data_time: 0.001, memory: 332, loss: 0.3220\n2023-01-28 14:51:01,176 - mmcls - INFO - Epoch [5][90/318]\tlr: 4.612e-03, eta: 0:01:46, time: 0.044, data_time: 0.001, memory: 332, loss: 0.2547\n2023-01-28 14:51:01,620 - mmcls - INFO - Epoch [5][100/318]\tlr: 4.612e-03, eta: 0:01:46, time: 0.049, data_time: 0.005, memory: 332, loss: 0.1557\n2023-01-28 14:51:02,095 - mmcls - INFO - Epoch [5][110/318]\tlr: 4.612e-03, eta: 0:01:45, time: 0.047, data_time: 0.001, memory: 332, loss: 0.4481\n2023-01-28 14:51:02,614 - mmcls - INFO - Epoch [5][120/318]\tlr: 4.612e-03, eta: 0:01:44, time: 0.052, data_time: 0.001, memory: 332, loss: 0.2754\n2023-01-28 14:51:03,119 - mmcls - INFO - Epoch [5][130/318]\tlr: 4.612e-03, eta: 0:01:43, time: 0.050, data_time: 0.001, memory: 332, loss: 0.3651\n2023-01-28 14:51:03,600 - mmcls - INFO - Epoch [5][140/318]\tlr: 4.612e-03, eta: 0:01:43, time: 0.048, data_time: 0.004, memory: 332, loss: 0.0725\n2023-01-28 14:51:04,092 - mmcls - INFO - Epoch [5][150/318]\tlr: 4.612e-03, eta: 0:01:42, time: 0.049, data_time: 0.001, memory: 332, loss: 0.1786\n2023-01-28 14:51:04,623 - mmcls - INFO - Epoch [5][160/318]\tlr: 4.612e-03, eta: 0:01:41, time: 0.053, data_time: 0.004, memory: 332, loss: 0.2270\n2023-01-28 14:51:05,374 - mmcls - INFO - Epoch [5][170/318]\tlr: 4.612e-03, eta: 0:01:41, time: 0.076, data_time: 0.001, memory: 332, loss: 0.3599\n2023-01-28 14:51:06,046 - mmcls - INFO - Epoch [5][180/318]\tlr: 4.612e-03, eta: 0:01:41, time: 0.067, data_time: 0.001, memory: 332, loss: 0.2305\n2023-01-28 14:51:06,690 - mmcls - INFO - Epoch [5][190/318]\tlr: 4.612e-03, eta: 0:01:40, time: 0.064, data_time: 0.001, memory: 332, loss: 0.4356\n2023-01-28 14:51:07,468 - mmcls - INFO - Epoch [5][200/318]\tlr: 4.612e-03, eta: 0:01:40, time: 0.078, data_time: 0.001, memory: 332, loss: 0.3119\n2023-01-28 14:51:08,222 - mmcls - INFO - Epoch [5][210/318]\tlr: 4.612e-03, eta: 0:01:39, time: 0.074, data_time: 0.000, memory: 332, loss: 0.2280\n2023-01-28 14:51:09,091 - mmcls - INFO - Epoch [5][220/318]\tlr: 4.612e-03, eta: 0:01:39, time: 0.088, data_time: 0.002, memory: 332, loss: 0.1440\n2023-01-28 14:51:09,587 - mmcls - INFO - Epoch [5][230/318]\tlr: 4.612e-03, eta: 0:01:38, time: 0.050, data_time: 0.001, memory: 332, loss: 0.6478\n2023-01-28 14:51:10,044 - mmcls - INFO - Epoch [5][240/318]\tlr: 4.612e-03, eta: 0:01:38, time: 0.046, data_time: 0.000, memory: 332, loss: 0.3494\n2023-01-28 14:51:10,531 - mmcls - INFO - Epoch [5][250/318]\tlr: 4.612e-03, eta: 0:01:37, time: 0.049, data_time: 0.003, memory: 332, loss: 0.4765\n2023-01-28 14:51:11,023 - mmcls - INFO - Epoch [5][260/318]\tlr: 4.612e-03, eta: 0:01:36, time: 0.049, data_time: 0.001, memory: 332, loss: 0.4443\n2023-01-28 14:51:11,671 - mmcls - INFO - Epoch [5][270/318]\tlr: 4.612e-03, eta: 0:01:36, time: 0.065, data_time: 0.001, memory: 332, loss: 0.1925\n2023-01-28 14:51:12,354 - mmcls - INFO - Epoch [5][280/318]\tlr: 4.612e-03, eta: 0:01:35, time: 0.067, data_time: 0.001, memory: 332, loss: 0.3269\n2023-01-28 14:51:12,811 - mmcls - INFO - Epoch [5][290/318]\tlr: 4.612e-03, eta: 0:01:34, time: 0.047, data_time: 0.002, memory: 332, loss: 0.5774\n2023-01-28 14:51:13,302 - mmcls - INFO - Epoch [5][300/318]\tlr: 4.612e-03, eta: 0:01:34, time: 0.049, data_time: 0.001, memory: 332, loss: 0.3380\n2023-01-28 14:51:13,795 - mmcls - INFO - Epoch [5][310/318]\tlr: 4.612e-03, eta: 0:01:33, time: 0.049, data_time: 0.001, memory: 332, loss: 0.5039\n2023-01-28 14:51:14,191 - mmcls - INFO - Saving checkpoint at 5 epochs\n","name":"stderr"},{"output_type":"stream","text":"[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 159/159, 95.9 task/s, elapsed: 2s, ETA:     0s","name":"stdout"},{"output_type":"stream","text":"2023-01-28 14:51:15,955 - mmcls - INFO - Epoch(val) [5][40]\taccuracy_top-1: 91.8239\n2023-01-28 14:51:18,438 - mmcls - INFO - Epoch [6][10/318]\tlr: 4.520e-03, eta: 0:01:33, time: 0.248, data_time: 0.202, memory: 332, loss: 0.2130\n2023-01-28 14:51:18,996 - mmcls - INFO - Epoch [6][20/318]\tlr: 4.520e-03, eta: 0:01:33, time: 0.056, data_time: 0.004, memory: 332, loss: 0.3866\n2023-01-28 14:51:19,525 - mmcls - INFO - Epoch [6][30/318]\tlr: 4.520e-03, eta: 0:01:32, time: 0.053, data_time: 0.001, memory: 332, loss: 0.2331\n2023-01-28 14:51:20,027 - mmcls - INFO - Epoch [6][40/318]\tlr: 4.520e-03, eta: 0:01:31, time: 0.050, data_time: 0.001, memory: 332, loss: 0.2020\n2023-01-28 14:51:20,589 - mmcls - INFO - Epoch [6][50/318]\tlr: 4.520e-03, eta: 0:01:31, time: 0.056, data_time: 0.001, memory: 332, loss: 0.2989\n2023-01-28 14:51:21,037 - mmcls - INFO - Epoch [6][60/318]\tlr: 4.520e-03, eta: 0:01:30, time: 0.045, data_time: 0.000, memory: 332, loss: 0.3096\n2023-01-28 14:51:21,516 - mmcls - INFO - Epoch [6][70/318]\tlr: 4.520e-03, eta: 0:01:29, time: 0.048, data_time: 0.001, memory: 332, loss: 0.2008\n2023-01-28 14:51:21,989 - mmcls - INFO - Epoch [6][80/318]\tlr: 4.520e-03, eta: 0:01:29, time: 0.048, data_time: 0.001, memory: 332, loss: 0.2467\n2023-01-28 14:51:22,428 - mmcls - INFO - Epoch [6][90/318]\tlr: 4.520e-03, eta: 0:01:28, time: 0.044, data_time: 0.001, memory: 332, loss: 0.2679\n2023-01-28 14:51:22,922 - mmcls - INFO - Epoch [6][100/318]\tlr: 4.520e-03, eta: 0:01:27, time: 0.050, data_time: 0.001, memory: 332, loss: 0.1646\n2023-01-28 14:51:23,392 - mmcls - INFO - Epoch [6][110/318]\tlr: 4.520e-03, eta: 0:01:27, time: 0.047, data_time: 0.001, memory: 332, loss: 0.0640\n2023-01-28 14:51:23,832 - mmcls - INFO - Epoch [6][120/318]\tlr: 4.520e-03, eta: 0:01:26, time: 0.044, data_time: 0.000, memory: 332, loss: 0.4755\n2023-01-28 14:51:24,319 - mmcls - INFO - Epoch [6][130/318]\tlr: 4.520e-03, eta: 0:01:25, time: 0.049, data_time: 0.001, memory: 332, loss: 0.3614\n2023-01-28 14:51:24,798 - mmcls - INFO - Epoch [6][140/318]\tlr: 4.520e-03, eta: 0:01:25, time: 0.048, data_time: 0.001, memory: 332, loss: 0.3452\n2023-01-28 14:51:25,234 - mmcls - INFO - Epoch [6][150/318]\tlr: 4.520e-03, eta: 0:01:24, time: 0.044, data_time: 0.001, memory: 332, loss: 0.2061\n2023-01-28 14:51:25,718 - mmcls - INFO - Epoch [6][160/318]\tlr: 4.520e-03, eta: 0:01:23, time: 0.048, data_time: 0.001, memory: 332, loss: 0.3296\n2023-01-28 14:51:26,197 - mmcls - INFO - Epoch [6][170/318]\tlr: 4.520e-03, eta: 0:01:22, time: 0.048, data_time: 0.001, memory: 332, loss: 0.3169\n2023-01-28 14:51:26,686 - mmcls - INFO - Epoch [6][180/318]\tlr: 4.520e-03, eta: 0:01:22, time: 0.049, data_time: 0.000, memory: 332, loss: 0.2418\n2023-01-28 14:51:27,137 - mmcls - INFO - Epoch [6][190/318]\tlr: 4.520e-03, eta: 0:01:21, time: 0.045, data_time: 0.000, memory: 332, loss: 0.1463\n2023-01-28 14:51:27,695 - mmcls - INFO - Epoch [6][200/318]\tlr: 4.520e-03, eta: 0:01:21, time: 0.056, data_time: 0.000, memory: 332, loss: 0.0466\n2023-01-28 14:51:28,113 - mmcls - INFO - Epoch [6][210/318]\tlr: 4.520e-03, eta: 0:01:20, time: 0.042, data_time: 0.000, memory: 332, loss: 0.1390\n2023-01-28 14:51:28,599 - mmcls - INFO - Epoch [6][220/318]\tlr: 4.520e-03, eta: 0:01:19, time: 0.048, data_time: 0.001, memory: 332, loss: 0.5429\n2023-01-28 14:51:29,094 - mmcls - INFO - Epoch [6][230/318]\tlr: 4.520e-03, eta: 0:01:19, time: 0.050, data_time: 0.001, memory: 332, loss: 0.5087\n2023-01-28 14:51:29,602 - mmcls - INFO - Epoch [6][240/318]\tlr: 4.520e-03, eta: 0:01:18, time: 0.050, data_time: 0.001, memory: 332, loss: 0.2702\n2023-01-28 14:51:30,081 - mmcls - INFO - Epoch [6][250/318]\tlr: 4.520e-03, eta: 0:01:17, time: 0.049, data_time: 0.002, memory: 332, loss: 0.3212\n2023-01-28 14:51:30,537 - mmcls - INFO - Epoch [6][260/318]\tlr: 4.520e-03, eta: 0:01:17, time: 0.046, data_time: 0.001, memory: 332, loss: 0.4072\n2023-01-28 14:51:31,082 - mmcls - INFO - Epoch [6][270/318]\tlr: 4.520e-03, eta: 0:01:16, time: 0.055, data_time: 0.001, memory: 332, loss: 0.2616\n2023-01-28 14:51:31,637 - mmcls - INFO - Epoch [6][280/318]\tlr: 4.520e-03, eta: 0:01:15, time: 0.056, data_time: 0.001, memory: 332, loss: 0.3110\n2023-01-28 14:51:32,422 - mmcls - INFO - Epoch [6][290/318]\tlr: 4.520e-03, eta: 0:01:15, time: 0.078, data_time: 0.001, memory: 332, loss: 0.3377\n2023-01-28 14:51:33,206 - mmcls - INFO - Epoch [6][300/318]\tlr: 4.520e-03, eta: 0:01:14, time: 0.077, data_time: 0.001, memory: 332, loss: 0.1488\n2023-01-28 14:51:33,771 - mmcls - INFO - Epoch [6][310/318]\tlr: 4.520e-03, eta: 0:01:14, time: 0.057, data_time: 0.001, memory: 332, loss: 0.4791\n2023-01-28 14:51:34,209 - mmcls - INFO - Saving checkpoint at 6 epochs\n","name":"stderr"},{"output_type":"stream","text":"[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 159/159, 149.6 task/s, elapsed: 1s, ETA:     0s","name":"stdout"},{"output_type":"stream","text":"2023-01-28 14:51:35,407 - mmcls - INFO - The previous best checkpoint /home/openinnolab/work/current/checkpoints/cls_model/tudou_mobilenet_6/best_accuracy_top-1_epoch_4.pth was removed\n2023-01-28 14:51:35,503 - mmcls - INFO - Now best checkpoint is saved as best_accuracy_top-1_epoch_6.pth.\n2023-01-28 14:51:35,504 - mmcls - INFO - Best accuracy_top-1 is 98.1132 at 6 epoch.\n2023-01-28 14:51:35,505 - mmcls - INFO - Epoch(val) [6][40]\taccuracy_top-1: 98.1132\n2023-01-28 14:51:38,011 - mmcls - INFO - Epoch [7][10/318]\tlr: 4.429e-03, eta: 0:01:14, time: 0.249, data_time: 0.202, memory: 332, loss: 0.4987\n2023-01-28 14:51:38,495 - mmcls - INFO - Epoch [7][20/318]\tlr: 4.429e-03, eta: 0:01:13, time: 0.049, data_time: 0.001, memory: 332, loss: 0.3089\n2023-01-28 14:51:38,991 - mmcls - INFO - Epoch [7][30/318]\tlr: 4.429e-03, eta: 0:01:13, time: 0.050, data_time: 0.001, memory: 332, loss: 0.3294\n2023-01-28 14:51:39,443 - mmcls - INFO - Epoch [7][40/318]\tlr: 4.429e-03, eta: 0:01:12, time: 0.044, data_time: 0.001, memory: 332, loss: 0.2966\n2023-01-28 14:51:39,942 - mmcls - INFO - Epoch [7][50/318]\tlr: 4.429e-03, eta: 0:01:11, time: 0.051, data_time: 0.002, memory: 332, loss: 0.1399\n2023-01-28 14:51:40,489 - mmcls - INFO - Epoch [7][60/318]\tlr: 4.429e-03, eta: 0:01:11, time: 0.055, data_time: 0.004, memory: 332, loss: 0.2597\n2023-01-28 14:51:40,994 - mmcls - INFO - Epoch [7][70/318]\tlr: 4.429e-03, eta: 0:01:10, time: 0.050, data_time: 0.001, memory: 332, loss: 0.2992\n2023-01-28 14:51:41,493 - mmcls - INFO - Epoch [7][80/318]\tlr: 4.429e-03, eta: 0:01:09, time: 0.050, data_time: 0.001, memory: 332, loss: 0.1514\n2023-01-28 14:51:41,996 - mmcls - INFO - Epoch [7][90/318]\tlr: 4.429e-03, eta: 0:01:09, time: 0.050, data_time: 0.001, memory: 332, loss: 0.1977\n2023-01-28 14:51:42,497 - mmcls - INFO - Epoch [7][100/318]\tlr: 4.429e-03, eta: 0:01:08, time: 0.050, data_time: 0.001, memory: 332, loss: 0.3661\n2023-01-28 14:51:43,013 - mmcls - INFO - Epoch [7][110/318]\tlr: 4.429e-03, eta: 0:01:07, time: 0.051, data_time: 0.001, memory: 332, loss: 0.2029\n2023-01-28 14:51:43,510 - mmcls - INFO - Epoch [7][120/318]\tlr: 4.429e-03, eta: 0:01:07, time: 0.051, data_time: 0.009, memory: 332, loss: 0.2174\n2023-01-28 14:51:44,007 - mmcls - INFO - Epoch [7][130/318]\tlr: 4.429e-03, eta: 0:01:06, time: 0.049, data_time: 0.004, memory: 332, loss: 0.2783\n2023-01-28 14:51:44,499 - mmcls - INFO - Epoch [7][140/318]\tlr: 4.429e-03, eta: 0:01:06, time: 0.050, data_time: 0.006, memory: 332, loss: 0.3930\n2023-01-28 14:51:45,079 - mmcls - INFO - Epoch [7][150/318]\tlr: 4.429e-03, eta: 0:01:05, time: 0.058, data_time: 0.001, memory: 332, loss: 0.2714\n2023-01-28 14:51:45,679 - mmcls - INFO - Epoch [7][160/318]\tlr: 4.429e-03, eta: 0:01:04, time: 0.060, data_time: 0.000, memory: 332, loss: 0.4740\n2023-01-28 14:51:46,230 - mmcls - INFO - Epoch [7][170/318]\tlr: 4.429e-03, eta: 0:01:04, time: 0.055, data_time: 0.001, memory: 332, loss: 0.5569\n2023-01-28 14:51:46,742 - mmcls - INFO - Epoch [7][180/318]\tlr: 4.429e-03, eta: 0:01:03, time: 0.051, data_time: 0.001, memory: 332, loss: 0.5948\n2023-01-28 14:51:47,312 - mmcls - INFO - Epoch [7][190/318]\tlr: 4.429e-03, eta: 0:01:03, time: 0.056, data_time: 0.001, memory: 332, loss: 0.4812\n2023-01-28 14:51:47,916 - mmcls - INFO - Epoch [7][200/318]\tlr: 4.429e-03, eta: 0:01:02, time: 0.061, data_time: 0.002, memory: 332, loss: 0.2994\n2023-01-28 14:51:48,488 - mmcls - INFO - Epoch [7][210/318]\tlr: 4.429e-03, eta: 0:01:01, time: 0.057, data_time: 0.000, memory: 332, loss: 0.2246\n2023-01-28 14:51:49,004 - mmcls - INFO - Epoch [7][220/318]\tlr: 4.429e-03, eta: 0:01:01, time: 0.052, data_time: 0.001, memory: 332, loss: 0.5288\n2023-01-28 14:51:49,513 - mmcls - INFO - Epoch [7][230/318]\tlr: 4.429e-03, eta: 0:01:00, time: 0.051, data_time: 0.000, memory: 332, loss: 0.4157\n2023-01-28 14:51:50,044 - mmcls - INFO - Epoch [7][240/318]\tlr: 4.429e-03, eta: 0:01:00, time: 0.053, data_time: 0.000, memory: 332, loss: 0.3178\n2023-01-28 14:51:50,528 - mmcls - INFO - Epoch [7][250/318]\tlr: 4.429e-03, eta: 0:00:59, time: 0.048, data_time: 0.001, memory: 332, loss: 0.3751\n2023-01-28 14:51:51,076 - mmcls - INFO - Epoch [7][260/318]\tlr: 4.429e-03, eta: 0:00:58, time: 0.050, data_time: 0.001, memory: 332, loss: 0.3583\n2023-01-28 14:51:51,524 - mmcls - INFO - Epoch [7][270/318]\tlr: 4.429e-03, eta: 0:00:58, time: 0.049, data_time: 0.005, memory: 332, loss: 0.2598\n2023-01-28 14:51:52,020 - mmcls - INFO - Epoch [7][280/318]\tlr: 4.429e-03, eta: 0:00:57, time: 0.050, data_time: 0.001, memory: 332, loss: 0.2814\n2023-01-28 14:51:52,501 - mmcls - INFO - Epoch [7][290/318]\tlr: 4.429e-03, eta: 0:00:56, time: 0.048, data_time: 0.001, memory: 332, loss: 0.1838\n2023-01-28 14:51:52,976 - mmcls - INFO - Epoch [7][300/318]\tlr: 4.429e-03, eta: 0:00:56, time: 0.048, data_time: 0.001, memory: 332, loss: 0.2004\n2023-01-28 14:51:53,421 - mmcls - INFO - Epoch [7][310/318]\tlr: 4.429e-03, eta: 0:00:55, time: 0.044, data_time: 0.000, memory: 332, loss: 0.4346\n2023-01-28 14:51:53,738 - mmcls - INFO - Saving checkpoint at 7 epochs\n","name":"stderr"},{"output_type":"stream","text":"[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 159/159, 136.3 task/s, elapsed: 1s, ETA:     0s","name":"stdout"},{"output_type":"stream","text":"2023-01-28 14:51:55,015 - mmcls - INFO - Epoch(val) [7][40]\taccuracy_top-1: 84.9057\n2023-01-28 14:51:57,499 - mmcls - INFO - Epoch [8][10/318]\tlr: 4.341e-03, eta: 0:00:55, time: 0.248, data_time: 0.202, memory: 332, loss: 0.4512\n2023-01-28 14:51:57,933 - mmcls - INFO - Epoch [8][20/318]\tlr: 4.341e-03, eta: 0:00:54, time: 0.044, data_time: 0.001, memory: 332, loss: 0.3392\n2023-01-28 14:51:58,404 - mmcls - INFO - Epoch [8][30/318]\tlr: 4.341e-03, eta: 0:00:53, time: 0.047, data_time: 0.000, memory: 332, loss: 0.3278\n2023-01-28 14:51:58,837 - mmcls - INFO - Epoch [8][40/318]\tlr: 4.341e-03, eta: 0:00:53, time: 0.043, data_time: 0.000, memory: 332, loss: 0.2463\n2023-01-28 14:51:59,305 - mmcls - INFO - Epoch [8][50/318]\tlr: 4.341e-03, eta: 0:00:52, time: 0.047, data_time: 0.004, memory: 332, loss: 0.3029\n2023-01-28 14:51:59,742 - mmcls - INFO - Epoch [8][60/318]\tlr: 4.341e-03, eta: 0:00:52, time: 0.044, data_time: 0.000, memory: 332, loss: 0.3087\n2023-01-28 14:52:00,206 - mmcls - INFO - Epoch [8][70/318]\tlr: 4.341e-03, eta: 0:00:51, time: 0.046, data_time: 0.004, memory: 332, loss: 0.2189\n2023-01-28 14:52:00,684 - mmcls - INFO - Epoch [8][80/318]\tlr: 4.341e-03, eta: 0:00:50, time: 0.048, data_time: 0.000, memory: 332, loss: 0.2861\n2023-01-28 14:52:01,110 - mmcls - INFO - Epoch [8][90/318]\tlr: 4.341e-03, eta: 0:00:50, time: 0.043, data_time: 0.001, memory: 332, loss: 0.2287\n2023-01-28 14:52:01,577 - mmcls - INFO - Epoch [8][100/318]\tlr: 4.341e-03, eta: 0:00:49, time: 0.047, data_time: 0.001, memory: 332, loss: 0.0841\n2023-01-28 14:52:02,008 - mmcls - INFO - Epoch [8][110/318]\tlr: 4.341e-03, eta: 0:00:48, time: 0.043, data_time: 0.001, memory: 332, loss: 0.3566\n2023-01-28 14:52:02,489 - mmcls - INFO - Epoch [8][120/318]\tlr: 4.341e-03, eta: 0:00:48, time: 0.048, data_time: 0.001, memory: 332, loss: 0.2583\n2023-01-28 14:52:02,918 - mmcls - INFO - Epoch [8][130/318]\tlr: 4.341e-03, eta: 0:00:47, time: 0.043, data_time: 0.001, memory: 332, loss: 0.2646\n2023-01-28 14:52:03,387 - mmcls - INFO - Epoch [8][140/318]\tlr: 4.341e-03, eta: 0:00:47, time: 0.047, data_time: 0.000, memory: 332, loss: 0.3713\n2023-01-28 14:52:03,831 - mmcls - INFO - Epoch [8][150/318]\tlr: 4.341e-03, eta: 0:00:46, time: 0.044, data_time: 0.000, memory: 332, loss: 0.2396\n2023-01-28 14:52:04,303 - mmcls - INFO - Epoch [8][160/318]\tlr: 4.341e-03, eta: 0:00:45, time: 0.047, data_time: 0.000, memory: 332, loss: 0.4435\n2023-01-28 14:52:04,776 - mmcls - INFO - Epoch [8][170/318]\tlr: 4.341e-03, eta: 0:00:45, time: 0.047, data_time: 0.000, memory: 332, loss: 0.2085\n2023-01-28 14:52:05,215 - mmcls - INFO - Epoch [8][180/318]\tlr: 4.341e-03, eta: 0:00:44, time: 0.044, data_time: 0.000, memory: 332, loss: 0.1971\n2023-01-28 14:52:05,683 - mmcls - INFO - Epoch [8][190/318]\tlr: 4.341e-03, eta: 0:00:43, time: 0.047, data_time: 0.000, memory: 332, loss: 0.2636\n2023-01-28 14:52:06,121 - mmcls - INFO - Epoch [8][200/318]\tlr: 4.341e-03, eta: 0:00:43, time: 0.044, data_time: 0.000, memory: 332, loss: 0.2375\n2023-01-28 14:52:06,600 - mmcls - INFO - Epoch [8][210/318]\tlr: 4.341e-03, eta: 0:00:42, time: 0.048, data_time: 0.001, memory: 332, loss: 0.0915\n2023-01-28 14:52:07,032 - mmcls - INFO - Epoch [8][220/318]\tlr: 4.341e-03, eta: 0:00:42, time: 0.043, data_time: 0.001, memory: 332, loss: 0.0894\n2023-01-28 14:52:07,501 - mmcls - INFO - Epoch [8][230/318]\tlr: 4.341e-03, eta: 0:00:41, time: 0.047, data_time: 0.000, memory: 332, loss: 0.1552\n2023-01-28 14:52:07,983 - mmcls - INFO - Epoch [8][240/318]\tlr: 4.341e-03, eta: 0:00:40, time: 0.048, data_time: 0.000, memory: 332, loss: 0.2260\n2023-01-28 14:52:08,422 - mmcls - INFO - Epoch [8][250/318]\tlr: 4.341e-03, eta: 0:00:40, time: 0.044, data_time: 0.000, memory: 332, loss: 0.2341\n2023-01-28 14:52:08,900 - mmcls - INFO - Epoch [8][260/318]\tlr: 4.341e-03, eta: 0:00:39, time: 0.048, data_time: 0.001, memory: 332, loss: 0.2267\n2023-01-28 14:52:09,329 - mmcls - INFO - Epoch [8][270/318]\tlr: 4.341e-03, eta: 0:00:39, time: 0.043, data_time: 0.001, memory: 332, loss: 0.1939\n2023-01-28 14:52:09,816 - mmcls - INFO - Epoch [8][280/318]\tlr: 4.341e-03, eta: 0:00:38, time: 0.049, data_time: 0.001, memory: 332, loss: 0.1320\n2023-01-28 14:52:10,279 - mmcls - INFO - Epoch [8][290/318]\tlr: 4.341e-03, eta: 0:00:37, time: 0.046, data_time: 0.000, memory: 332, loss: 1.2833\n2023-01-28 14:52:10,713 - mmcls - INFO - Epoch [8][300/318]\tlr: 4.341e-03, eta: 0:00:37, time: 0.043, data_time: 0.001, memory: 332, loss: 0.5114\n2023-01-28 14:52:11,186 - mmcls - INFO - Epoch [8][310/318]\tlr: 4.341e-03, eta: 0:00:36, time: 0.047, data_time: 0.001, memory: 332, loss: 0.2127\n2023-01-28 14:52:11,482 - mmcls - INFO - Saving checkpoint at 8 epochs\n","name":"stderr"},{"output_type":"stream","text":"[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 159/159, 153.8 task/s, elapsed: 1s, ETA:     0s","name":"stdout"},{"output_type":"stream","text":"2023-01-28 14:52:12,623 - mmcls - INFO - Epoch(val) [8][40]\taccuracy_top-1: 90.5660\n2023-01-28 14:52:15,097 - mmcls - INFO - Epoch [9][10/318]\tlr: 4.254e-03, eta: 0:00:36, time: 0.247, data_time: 0.202, memory: 332, loss: 0.3958\n2023-01-28 14:52:15,583 - mmcls - INFO - Epoch [9][20/318]\tlr: 4.254e-03, eta: 0:00:35, time: 0.049, data_time: 0.000, memory: 332, loss: 0.2630\n2023-01-28 14:52:16,023 - mmcls - INFO - Epoch [9][30/318]\tlr: 4.254e-03, eta: 0:00:34, time: 0.044, data_time: 0.000, memory: 332, loss: 0.2618\n2023-01-28 14:52:16,499 - mmcls - INFO - Epoch [9][40/318]\tlr: 4.254e-03, eta: 0:00:34, time: 0.048, data_time: 0.001, memory: 332, loss: 0.3009\n2023-01-28 14:52:16,988 - mmcls - INFO - Epoch [9][50/318]\tlr: 4.254e-03, eta: 0:00:33, time: 0.049, data_time: 0.000, memory: 332, loss: 0.2891\n2023-01-28 14:52:17,428 - mmcls - INFO - Epoch [9][60/318]\tlr: 4.254e-03, eta: 0:00:33, time: 0.044, data_time: 0.001, memory: 332, loss: 0.3326\n2023-01-28 14:52:17,921 - mmcls - INFO - Epoch [9][70/318]\tlr: 4.254e-03, eta: 0:00:32, time: 0.049, data_time: 0.001, memory: 332, loss: 0.1788\n2023-01-28 14:52:18,429 - mmcls - INFO - Epoch [9][80/318]\tlr: 4.254e-03, eta: 0:00:31, time: 0.051, data_time: 0.000, memory: 332, loss: 0.3192\n2023-01-28 14:52:18,939 - mmcls - INFO - Epoch [9][90/318]\tlr: 4.254e-03, eta: 0:00:31, time: 0.051, data_time: 0.000, memory: 332, loss: 0.1202\n2023-01-28 14:52:19,439 - mmcls - INFO - Epoch [9][100/318]\tlr: 4.254e-03, eta: 0:00:30, time: 0.050, data_time: 0.000, memory: 332, loss: 0.2016\n2023-01-28 14:52:19,925 - mmcls - INFO - Epoch [9][110/318]\tlr: 4.254e-03, eta: 0:00:30, time: 0.049, data_time: 0.000, memory: 332, loss: 0.3692\n2023-01-28 14:52:20,392 - mmcls - INFO - Epoch [9][120/318]\tlr: 4.254e-03, eta: 0:00:29, time: 0.047, data_time: 0.000, memory: 332, loss: 0.5053\n2023-01-28 14:52:20,827 - mmcls - INFO - Epoch [9][130/318]\tlr: 4.254e-03, eta: 0:00:28, time: 0.044, data_time: 0.000, memory: 332, loss: 0.4554\n2023-01-28 14:52:21,307 - mmcls - INFO - Epoch [9][140/318]\tlr: 4.254e-03, eta: 0:00:28, time: 0.048, data_time: 0.000, memory: 332, loss: 0.2856\n2023-01-28 14:52:21,738 - mmcls - INFO - Epoch [9][150/318]\tlr: 4.254e-03, eta: 0:00:27, time: 0.043, data_time: 0.000, memory: 332, loss: 0.4006\n2023-01-28 14:52:22,205 - mmcls - INFO - Epoch [9][160/318]\tlr: 4.254e-03, eta: 0:00:27, time: 0.047, data_time: 0.004, memory: 332, loss: 0.3720\n2023-01-28 14:52:22,676 - mmcls - INFO - Epoch [9][170/318]\tlr: 4.254e-03, eta: 0:00:26, time: 0.044, data_time: 0.000, memory: 332, loss: 0.3521\n2023-01-28 14:52:23,105 - mmcls - INFO - Epoch [9][180/318]\tlr: 4.254e-03, eta: 0:00:25, time: 0.046, data_time: 0.004, memory: 332, loss: 0.1576\n2023-01-28 14:52:23,578 - mmcls - INFO - Epoch [9][190/318]\tlr: 4.254e-03, eta: 0:00:25, time: 0.047, data_time: 0.001, memory: 332, loss: 0.3530\n2023-01-28 14:52:24,021 - mmcls - INFO - Epoch [9][200/318]\tlr: 4.254e-03, eta: 0:00:24, time: 0.044, data_time: 0.001, memory: 332, loss: 0.1928\n2023-01-28 14:52:24,510 - mmcls - INFO - Epoch [9][210/318]\tlr: 4.254e-03, eta: 0:00:24, time: 0.049, data_time: 0.000, memory: 332, loss: 0.1419\n2023-01-28 14:52:24,989 - mmcls - INFO - Epoch [9][220/318]\tlr: 4.254e-03, eta: 0:00:23, time: 0.048, data_time: 0.001, memory: 332, loss: 0.3049\n2023-01-28 14:52:25,423 - mmcls - INFO - Epoch [9][230/318]\tlr: 4.254e-03, eta: 0:00:23, time: 0.043, data_time: 0.000, memory: 332, loss: 0.1589\n2023-01-28 14:52:25,910 - mmcls - INFO - Epoch [9][240/318]\tlr: 4.254e-03, eta: 0:00:22, time: 0.049, data_time: 0.001, memory: 332, loss: 0.1569\n2023-01-28 14:52:26,391 - mmcls - INFO - Epoch [9][250/318]\tlr: 4.254e-03, eta: 0:00:21, time: 0.048, data_time: 0.004, memory: 332, loss: 0.2049\n2023-01-28 14:52:26,838 - mmcls - INFO - Epoch [9][260/318]\tlr: 4.254e-03, eta: 0:00:21, time: 0.045, data_time: 0.001, memory: 332, loss: 0.1396\n2023-01-28 14:52:27,312 - mmcls - INFO - Epoch [9][270/318]\tlr: 4.254e-03, eta: 0:00:20, time: 0.047, data_time: 0.001, memory: 332, loss: 0.1130\n2023-01-28 14:52:27,789 - mmcls - INFO - Epoch [9][280/318]\tlr: 4.254e-03, eta: 0:00:20, time: 0.048, data_time: 0.000, memory: 332, loss: 0.1930\n2023-01-28 14:52:28,212 - mmcls - INFO - Epoch [9][290/318]\tlr: 4.254e-03, eta: 0:00:19, time: 0.042, data_time: 0.000, memory: 332, loss: 0.2237\n2023-01-28 14:52:28,696 - mmcls - INFO - Epoch [9][300/318]\tlr: 4.254e-03, eta: 0:00:18, time: 0.048, data_time: 0.000, memory: 332, loss: 0.5779\n2023-01-28 14:52:29,135 - mmcls - INFO - Epoch [9][310/318]\tlr: 4.254e-03, eta: 0:00:18, time: 0.044, data_time: 0.001, memory: 332, loss: 0.4104\n2023-01-28 14:52:29,523 - mmcls - INFO - Saving checkpoint at 9 epochs\n","name":"stderr"},{"output_type":"stream","text":"[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 159/159, 149.8 task/s, elapsed: 1s, ETA:     0s","name":"stdout"},{"output_type":"stream","text":"2023-01-28 14:52:30,682 - mmcls - INFO - Epoch(val) [9][40]\taccuracy_top-1: 98.1132\n2023-01-28 14:52:33,207 - mmcls - INFO - Epoch [10][10/318]\tlr: 4.169e-03, eta: 0:00:17, time: 0.252, data_time: 0.202, memory: 332, loss: 0.2566\n2023-01-28 14:52:33,686 - mmcls - INFO - Epoch [10][20/318]\tlr: 4.169e-03, eta: 0:00:16, time: 0.048, data_time: 0.001, memory: 332, loss: 0.1089\n2023-01-28 14:52:34,132 - mmcls - INFO - Epoch [10][30/318]\tlr: 4.169e-03, eta: 0:00:16, time: 0.045, data_time: 0.001, memory: 332, loss: 0.1920\n2023-01-28 14:52:34,602 - mmcls - INFO - Epoch [10][40/318]\tlr: 4.169e-03, eta: 0:00:15, time: 0.047, data_time: 0.001, memory: 332, loss: 0.2139\n2023-01-28 14:52:35,037 - mmcls - INFO - Epoch [10][50/318]\tlr: 4.169e-03, eta: 0:00:15, time: 0.044, data_time: 0.001, memory: 332, loss: 0.2749\n2023-01-28 14:52:35,513 - mmcls - INFO - Epoch [10][60/318]\tlr: 4.169e-03, eta: 0:00:14, time: 0.048, data_time: 0.001, memory: 332, loss: 0.4026\n2023-01-28 14:52:35,990 - mmcls - INFO - Epoch [10][70/318]\tlr: 4.169e-03, eta: 0:00:14, time: 0.048, data_time: 0.000, memory: 332, loss: 0.2068\n2023-01-28 14:52:36,415 - mmcls - INFO - Epoch [10][80/318]\tlr: 4.169e-03, eta: 0:00:13, time: 0.043, data_time: 0.000, memory: 332, loss: 0.3027\n2023-01-28 14:52:36,897 - mmcls - INFO - Epoch [10][90/318]\tlr: 4.169e-03, eta: 0:00:12, time: 0.048, data_time: 0.001, memory: 332, loss: 0.2694\n2023-01-28 14:52:37,327 - mmcls - INFO - Epoch [10][100/318]\tlr: 4.169e-03, eta: 0:00:12, time: 0.043, data_time: 0.001, memory: 332, loss: 0.2606\n2023-01-28 14:52:37,801 - mmcls - INFO - Epoch [10][110/318]\tlr: 4.169e-03, eta: 0:00:11, time: 0.047, data_time: 0.000, memory: 332, loss: 0.2254\n2023-01-28 14:52:38,288 - mmcls - INFO - Epoch [10][120/318]\tlr: 4.169e-03, eta: 0:00:11, time: 0.049, data_time: 0.001, memory: 332, loss: 0.1012\n2023-01-28 14:52:38,719 - mmcls - INFO - Epoch [10][130/318]\tlr: 4.169e-03, eta: 0:00:10, time: 0.043, data_time: 0.001, memory: 332, loss: 0.1595\n2023-01-28 14:52:39,200 - mmcls - INFO - Epoch [10][140/318]\tlr: 4.169e-03, eta: 0:00:10, time: 0.048, data_time: 0.001, memory: 332, loss: 0.2316\n2023-01-28 14:52:39,639 - mmcls - INFO - Epoch [10][150/318]\tlr: 4.169e-03, eta: 0:00:09, time: 0.044, data_time: 0.000, memory: 332, loss: 0.2693\n2023-01-28 14:52:40,109 - mmcls - INFO - Epoch [10][160/318]\tlr: 4.169e-03, eta: 0:00:08, time: 0.047, data_time: 0.004, memory: 332, loss: 0.0966\n2023-01-28 14:52:40,585 - mmcls - INFO - Epoch [10][170/318]\tlr: 4.169e-03, eta: 0:00:08, time: 0.048, data_time: 0.001, memory: 332, loss: 0.2165\n2023-01-28 14:52:41,027 - mmcls - INFO - Epoch [10][180/318]\tlr: 4.169e-03, eta: 0:00:07, time: 0.044, data_time: 0.001, memory: 332, loss: 0.0447\n2023-01-28 14:52:41,506 - mmcls - INFO - Epoch [10][190/318]\tlr: 4.169e-03, eta: 0:00:07, time: 0.048, data_time: 0.001, memory: 332, loss: 0.5274\n2023-01-28 14:52:41,992 - mmcls - INFO - Epoch [10][200/318]\tlr: 4.169e-03, eta: 0:00:06, time: 0.049, data_time: 0.001, memory: 332, loss: 0.4123\n2023-01-28 14:52:42,428 - mmcls - INFO - Epoch [10][210/318]\tlr: 4.169e-03, eta: 0:00:06, time: 0.044, data_time: 0.001, memory: 332, loss: 0.1331\n2023-01-28 14:52:42,919 - mmcls - INFO - Epoch [10][220/318]\tlr: 4.169e-03, eta: 0:00:05, time: 0.049, data_time: 0.001, memory: 332, loss: 0.2395\n2023-01-28 14:52:43,393 - mmcls - INFO - Epoch [10][230/318]\tlr: 4.169e-03, eta: 0:00:04, time: 0.047, data_time: 0.004, memory: 332, loss: 0.3617\n2023-01-28 14:52:43,834 - mmcls - INFO - Epoch [10][240/318]\tlr: 4.169e-03, eta: 0:00:04, time: 0.044, data_time: 0.005, memory: 332, loss: 0.2588\n2023-01-28 14:52:44,316 - mmcls - INFO - Epoch [10][250/318]\tlr: 4.169e-03, eta: 0:00:03, time: 0.048, data_time: 0.000, memory: 332, loss: 0.1701\n2023-01-28 14:52:44,791 - mmcls - INFO - Epoch [10][260/318]\tlr: 4.169e-03, eta: 0:00:03, time: 0.047, data_time: 0.000, memory: 332, loss: 0.1687\n2023-01-28 14:52:45,217 - mmcls - INFO - Epoch [10][270/318]\tlr: 4.169e-03, eta: 0:00:02, time: 0.043, data_time: 0.001, memory: 332, loss: 0.2806\n2023-01-28 14:52:45,693 - mmcls - INFO - Epoch [10][280/318]\tlr: 4.169e-03, eta: 0:00:02, time: 0.048, data_time: 0.001, memory: 332, loss: 0.2213\n2023-01-28 14:52:46,131 - mmcls - INFO - Epoch [10][290/318]\tlr: 4.169e-03, eta: 0:00:01, time: 0.044, data_time: 0.001, memory: 332, loss: 0.1191\n2023-01-28 14:52:46,615 - mmcls - INFO - Epoch [10][300/318]\tlr: 4.169e-03, eta: 0:00:01, time: 0.048, data_time: 0.001, memory: 332, loss: 0.0927\n2023-01-28 14:52:47,102 - mmcls - INFO - Epoch [10][310/318]\tlr: 4.169e-03, eta: 0:00:00, time: 0.049, data_time: 0.001, memory: 332, loss: 0.2741\n2023-01-28 14:52:47,411 - mmcls - INFO - Saving checkpoint at 10 epochs\n","name":"stderr"},{"output_type":"stream","text":"[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 159/159, 159.8 task/s, elapsed: 1s, ETA:     0s","name":"stdout"},{"output_type":"stream","text":"2023-01-28 14:52:48,526 - mmcls - INFO - Epoch(val) [10][40]\taccuracy_top-1: 97.4843\n","name":"stderr"}]},{"metadata":{},"cell_type":"markdown","source":"## 2 模型推理"},{"metadata":{"trusted":true},"cell_type":"code","source":"from MMEdu import MMClassification as cls\nmodel = cls(backbone='MobileNet')\ncheckpoint = 'checkpoints/cls_model/tudou_mobilenet_6/best_accuracy_top-1_epoch_6.pth'\nclass_path = '/data/PTVSMN/tudou_dataset/classes.txt'\nimg_path = '/data/PTVSMN/tudou_dataset/test_set/faya/24.jpg'\n\nresult = model.inference(image=img_path, show=True, class_path=class_path,checkpoint = checkpoint)\nmodel.print_result(result)","execution_count":17,"outputs":[{"output_type":"stream","text":"You can use  'device=cuda' to accelerate !\nYou can use  'device=cuda' to accelerate !\nload checkpoint from local path: /home/openinnolab/work/current/checkpoints/cls_model/tudou_mobilenet_6/best_accuracy_top-1_epoch_6.pth\n========= begin inference ==========\n\n========= finish inference ==========\n分类结果如下：\n[{'标签': 1, '置信度': 0.5732681751251221, '预测结果': 'faya'}]\n","name":"stdout"},{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"[{'标签': 1, '置信度': 0.5732681751251221, '预测结果': 'faya'}]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 256.01x256.01 with 1 Axes>","image/png":"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3 模型转换"},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install onnx\n!pip install onnxruntime\n!pip install onnxsim","execution_count":18,"outputs":[{"output_type":"stream","text":"Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nCollecting onnx\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/fa/03/d624ee112f933325217a7693aaf00ff33d31adc2b0f9adcd213dd06f6275/onnx-1.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.5 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.5/13.5 MB\u001b[0m \u001b[31m18.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hCollecting protobuf<4,>=3.20.2\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c7/df/ec3ecb8c940b36121c7b77c10acebf3d1c736498aa2f1fe3b6231ee44e76/protobuf-3.20.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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https://pypi.tuna.tsinghua.edu.cn/packages/d4/cf/3965bddbb4f1a61c49aacae0e78fd1fe36b5dc36c797b31f30cf07dcbbb7/mpmath-1.2.1-py3-none-any.whl (532 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m532.6/532.6 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hInstalling collected packages: mpmath, flatbuffers, sympy, humanfriendly, coloredlogs, onnxruntime\nSuccessfully installed coloredlogs-15.0.1 flatbuffers-23.1.21 humanfriendly-10.0 mpmath-1.2.1 onnxruntime-1.13.1 sympy-1.11.1\nLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nCollecting onnxsim\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a3/68/2c65647c2b004c7a64848279e960331c96513946435fffcc8c171509ebf9/onnxsim-0.4.13-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m5.9 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(2.13.0)\nInstalling collected packages: onnxsim\nSuccessfully installed onnxsim-0.4.13\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from MMEdu import MMClassification as cls\nmodel = cls(backbone='MobileNet')\ncheckpoint = 'checkpoints/cls_model/tudou_mobilenet_6/best_accuracy_top-1_epoch_6.pth'\nmodel.num_classes = 2\nclass_path = '/data/PTVSMN/tudou_dataset/classes.txt'\nout_file='out_file/tudou.onnx'\nmodel.convert(checkpoint=checkpoint, backend=\"ONNX\", out_file=out_file, class_path=class_path)","execution_count":21,"outputs":[{"output_type":"stream","text":"load checkpoint from local path: checkpoints/cls_model/tudou_mobilenet_6/best_accuracy_top-1_epoch_6.pth\nSuccessfully exported ONNX model: out_file/tudou.onnx\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## 4 模型转换测试"},{"metadata":{},"cell_type":"markdown","source":"1)指定测试土豆图片是否发芽"},{"metadata":{"trusted":true},"cell_type":"code","source":"import onnxruntime as rt\nimport BaseData\nimport numpy as np\nimport cv2\n\ntag = ['0faya', 'faya']\nsess = rt.InferenceSession('out_file/tudou.onnx', None)\ninput_name = sess.get_inputs()[0].name\nout_name = sess.get_outputs()[0].name\n\ndt = BaseData.ImageData('/data/PTVSMN/tudou_dataset/test_set/faya/35.jpg', backbone='MobileNet')\ninput_data = dt.to_tensor()\n\npred_onx = sess.run([out_name], {input_name: input_data})\nort_output = pred_onx[0]\nidx = np.argmax(ort_output, axis=1)[0]\n\nif tag[idx] == 'faya':\n    print('土豆发芽了，不能吃！')\nelse:\n    print('土豆没发芽，可以吃！')","execution_count":22,"outputs":[{"output_type":"stream","text":"2023-01-28 15:03:21.382228626 [W:onnxruntime:, graph.cc:1231 Graph] Initializer head.fc.weight appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382272593 [W:onnxruntime:, graph.cc:1231 Graph] Initializer head.fc.bias appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382280712 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 544 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382287949 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 545 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382294205 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 547 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382300526 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 548 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382306980 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 550 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382313081 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 551 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382319383 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 553 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382325608 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 554 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382333298 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 556 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382339405 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 557 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382345670 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 559 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382352130 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 560 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382358462 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 562 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382364438 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 563 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382370887 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 565 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382377071 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 566 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382383515 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 568 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382390974 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 569 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382397249 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 571 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382403517 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 572 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382409705 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 574 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382415927 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 575 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382422256 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 577 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382428285 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 578 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382434586 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 580 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382440546 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 581 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382448061 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 583 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382454781 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 584 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382461750 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 586 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382467772 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 587 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382473979 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 589 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382480090 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 590 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382486188 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 592 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382492208 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 593 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382498685 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 595 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382506368 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 596 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382512583 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 598 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382518758 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 599 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382525179 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 601 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382531736 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 602 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382538047 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 604 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382544118 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 605 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382550230 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 607 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382556472 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 608 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382564322 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 610 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382570401 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 611 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382577248 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 613 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382583372 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 614 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382589706 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 616 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382595676 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 617 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382602597 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 619 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382608604 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 620 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382615258 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 622 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382623022 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 623 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382629444 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 625 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382635424 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 626 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382641714 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 628 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382649091 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 629 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382655588 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 631 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382661683 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 632 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382668219 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 634 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382674482 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 635 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382681939 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 637 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382687984 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 638 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382694359 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 640 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382700179 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 641 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382706492 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 643 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382712748 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 644 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382719090 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 646 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382725217 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 647 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382731581 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 649 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382738984 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 650 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382745293 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 652 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382751225 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 653 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382757363 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 655 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382763449 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 656 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382769783 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 658 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382775650 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 659 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382781797 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 661 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382787761 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 662 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382795541 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 664 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382801404 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 665 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382807712 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 667 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382813917 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 668 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382820129 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 670 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382826187 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 671 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382832461 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 673 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382838371 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 674 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382844492 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 676 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382851715 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 677 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382858084 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 679 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382864315 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 680 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382870256 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 682 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382876938 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 683 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382883326 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 685 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382889434 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 686 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382895639 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 688 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382901648 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 689 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382909624 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 691 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382916094 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 692 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382922412 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 694 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382928378 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 695 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382934672 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 697 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382940676 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 698 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n2023-01-28 15:03:21.382946723 [W:onnxruntime:, graph.cc:1231 Graph] Initializer 701 appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.\n","name":"stderr"},{"output_type":"stream","text":"土豆发芽了，不能吃！\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"2)如果行空板接入摄像头，用此代码可识别拍摄的土豆图片是否发芽"},{"metadata":{"trusted":true},"cell_type":"code","source":"import onnxruntime as rt\nimport BaseData\nimport numpy as np\nimport cv2\n\ntag = ['0faya', 'faya']\nsess = rt.InferenceSession('out_file/tudou.onnx', None)\ninput_name = sess.get_inputs()[0].name\nout_name = sess.get_outputs()[0].name\n\ncap = cv2.VideoCapture(0)\nret_flag,Vshow = cap.read()\ncap.release()\n\ndt = BaseData.ImageData(Vshow, backbone=\"MobileNet\")\ninput_data = dt.to_tensor()\n\npred_onx = sess.run([out_name], {input_name: input_data})\nort_output = pred_onx[0]\nidx = np.argmax(ort_output, axis=1)[0]\n\nif tag[idx] == 'faya':\n    print('土豆发芽了，不能吃！')\nelse:\n    print('土豆没发芽，可以吃！')","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"sskernel","display_name":"Python3","language":"python"},"language_info":{"name":"python","version":"3.9.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":5}